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Controlling for Fossil Record Bias in Macroevolutionary Analyses

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Controlling for Fossil Record Bias in Macroevolutionary Analyses bioRxiv preprint doi: https://doi.org/10.1101/726786; this version posted August 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Early Tetrapodomorph Biogeography: Controlling for Fossil Record 2 Bias in Macroevolutionary Analyses 3 4 Jacob D. Gardnera,1, Kevin Suryab,1, and Chris L. Organa* 5 6 a Department of Earth Sciences, Montana State University, Bozeman, MT 59717, USA 7 b Honors College, Montana State University, Bozeman, MT 59717, USA 8 9 * Corresponding author at: Department of Earth Sciences, Montana State University, Bozeman, 10 MT 59717, USA. E-mail address: [email protected] (C. L. Organ) 11 12 1 Contributed equally to this work 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 bioRxiv preprint doi: https://doi.org/10.1101/726786; this version posted August 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 47 ABSTRACT 48 The fossil record provides direct empirical data for understanding macroevolutionary patterns 49 and processes. Inherent biases in the fossil record are well known to confound analyses of this data. 50 Sampling bias proxies have been used as covariates in regression models to test for such biases. 51 Proxies, such as formation count, are associated with paleobiodiversity, but are insufficient for 52 explaining species dispersal owing to a lack of geographic context. Here, we develop a sampling 53 bias proxy that incorporates geographic information and test it with a case study on early 54 tetrapodomorph biogeography. We use recently-developed Bayesian phylogeographic models and 55 a new supertree of early tetrapodomorphs to estimate dispersal rates and ancestral habitat locations. 56 We find strong evidence that geographic sampling bias explains supposed radiations in dispersal 57 rate (potential adaptive radiations). Our study highlights the necessity of accounting for geographic 58 sampling bias in macroevolutionary and phylogenetic analyses and provides an approach to test 59 for its effect. 60 Keywords: sampling bias, fossil record, biogeography, phylogenetics, macroevolution, tetrapod 61 water-land transition 62 63 64 65 66 67 68 69 bioRxiv preprint doi: https://doi.org/10.1101/726786; this version posted August 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 2 70 1. Introduction 71 Our understanding of macroevolutionary patterns and processes are fundamentally based 72 on fossils. The most direct evidence for taxonomic origination and extinction rates come from the 73 rock record, as do evidence for novelty and climate change unseen in data sets gleaned from extant 74 sources. There are no perfect data sets in science; there are inherent limitations and biases in the 75 rock record that must be addressed when we form and test paleobiological hypotheses. For instance, 76 observed stratigraphic ranges of fossils can mislead inferences about diversification and extinction 77 rates (Raup and Boyajian, 1988; Signor and Lipps, 1982). Observed species diversity is also known 78 to increase with time due to the preferential preservation and recovery of fossils in younger 79 geological strata—referred to as "the Pull of the Recent" (Jablonski et al., 2003). Large and long- 80 surviving clades with high rates of early diversification tend to result in an illusionary rate slow- 81 down as diversification rates revert back to a mean value—referred to as “the Push of the Past” 82 (Budd and Mann, 2018). Paleobiologists test and account for these biases when analyzing 83 diversification and extinction at local and global scales (Alroy et al., 2001; Benson et al., 2010; 84 Benson and Butler, 2011; Benson and Upchurch, 2013; Benton et al., 2013; Foote, 2003; Jablonski 85 et al., 2003; Koch, 1978; Lloyd, 2012; Sakamoto et al., 2016a, 2016b). These bias-detection and 86 correction techniques include fossil occurrence subsampling (Alroy et al., 2001; Jablonski et al., 87 2003; Lloyd, 2012); correcting origination, extinction, and sampling rates using evolutionary 88 predictive models (Foote, 2003); the use of residuals from diversity-sampling models (Benson et 89 al., 2010; Benson and Upchurch, 2013; Sakamoto et al., 2016b); and the incorporation of sampling 90 bias proxies as covariates in regression models (Benson et al., 2010; Benson and Butler, 2011; 91 Benton et al., 2013; Sakamoto et al., 2016a). Benton et al. (2013), studying sampling bias proxies, 92 demonstrated that diversity through time closely tracks formation count (Benton et al., 2013). bioRxiv preprint doi: https://doi.org/10.1101/726786; this version posted August 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 3 93 However, case studies in England and Wales suggest that proxies for terrestrial sedimentary rock 94 volume (such as formation count) do not accurately explain paleobiodiversity, particularly if the 95 fossil record is patchy (Dunhill et al., 2014a, 2014b, 2013). Marine outcrop area and 96 paleoecological-associated facies changes are, however, associated with shifts in paleobiodiversity 97 (Dunhill et al., 2014b, 2013). Moreover, Benton et al. (2013) argue that the direction of causality 98 between paleobiodiversity and formation count is unclear; there may be a common cause to explain 99 their covariation, such as sea level (Benton et al., 2013). Nonetheless, formation count is a widely- 100 used sampling bias proxy in phylogenetic analyses of macroevolution (O’Donovan et al., 2018; 101 Sakamoto et al., 2016a, 2016b; Tennant et al., 2016a, 2016b). The advent of computational 102 modeling approaches, particularly phylogenetic comparative methods, has made it easier to 103 include proxies, like formation count, into models. Additional sampling bias proxies used in these 104 studies include occurrence count, valid taxon count, and specimen completeness and preservation 105 scores. Absent from these proxies is geographic context, which could confound many types of 106 macroevolutionary analyses. 107 Despite advancements made in understanding the origin and evolution of early 108 tetrapodomorphs, biogeographical studies are hindered by the incompleteness of the early 109 tetrapodomorph fossil record. For example, “Romer’s Gap” represents a lack of tetrapodomorph 110 fossils from the end-Devonian to mid-Mississippian, a period crucial for understanding early 111 tetrapodomorph diversification. Recent collection efforts recovered tetrapodomorph specimens 112 from “Romer’s Gap”, suggesting that a collection and preservation bias explains this gap (Clack 113 et al., 2017; Marshall et al., 2019). In addition, a trackway site in Poland demonstrates the existence 114 of digit-bearing tetrapodomorphs 10 million years before the earliest elpistostegalian body fossil, 115 showcasing the limitation of body fossils to reveal evolutionary history (Niedźwiedzki et al., 2010). bioRxiv preprint doi: https://doi.org/10.1101/726786; this version posted August 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 4 116 A recent study by Long et al. (2018) leveraged phylogenetic reconstruction of early 117 tetrapodomorphs to frame hypotheses about the origin of major clades, as well as their dispersal 118 patterns, including the hypothesis that stem-tetrapodomorphs dispersed from Eastern Gondwana 119 to Euramerica. However, this study did not use phylogenetic comparative methods to estimate 120 ancestral geographic locations or to model dispersal patterns. 121 Here, we present a phylogeographic analysis of early tetrapodomorphs. Our goals are: 1) 122 to construct a phylogenetic supertree of early tetrapodomorphs that synthesizes previous 123 phylogenetic reconstructions; 2) to estimate the paleogeographic locations of major early 124 tetrapodomorph clades using recently-developed phylogeographic models that account for the 125 curvature of the Earth; and 3) to test for the influence of geographic sampling bias on dispersal 126 rates. Our results indicate that geographic sampling bias substantially confounds analyses of 127 dispersal and paleogeography. We conclude with a discussion about the necessity of controlling 128 for fossil record biases in macroevolutionary analyses. 129 2. Materials and Methods 130 2.1. Nomenclature 131 Tetrapoda has been informally defined historically to include all terrestrial vertebrates with 132 limbs and digits (Laurin, 1998). Gauthier et al. (1989) first articulated a phylogenetic definition of 133 Tetrapoda as the clade including the last common ancestor of amniotes and lissamphibians. This 134 definition excludes stem-tetrapodomorphs, like Acanthostega and Ichthyostega. Stegocephalia 135 was coined by E.D. Cope in 1868 (Cope, 1868), but was more recently used to describe fossil taxa 136 more closely related to tetrapods than other sarcopterygians. A recent cladistic redefinition of 137 Stegocephalia includes all vertebrates more closely related to temnospondyls than Panderichthys 138 (Laurin, 1998). Here, we use the definitions of Laurin (1998) for a monophyletic Stegocephalia bioRxiv preprint doi: https://doi.org/10.1101/726786; this version posted August 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
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